The Boston startup uses AI to translate and verify legacy software for defense contractors, arguing modernization can't come at the cost of new bugs. Code Metal, a Boston-based startup that uses AI to write code and translate it into other programming languages, just closed a $125 million Series B funding round from new and existing investors. The news comes just a few months after the startup raised $36 million in series A financing led by Accel. Code Metal is part of a new wave of startups aiming to modernize the tech industry by using AI to generate code and translate it across programming languages. One of the questions that persists about AI-assisted code, though, is whether the output is any good--and what the consequences might be if it's not.

Gay men have long been rumored to run Silicon Valley. No one can say exactly when, or if, gay men started running Silicon Valley. They seem to have dominated its upper ranks at least the past five years, maybe more. On platforms like X, the clues are there: whispers of private-island retreats, tech executives going "gay for clout," and the suggestion that a "seed round" is not, strictly speaking, a financial term. It is an idea so taken for granted, in fact, that when I call up a well-connected hedge fund manager to ask his thoughts about what is sometimes referred to in industry circles as the "gay tech mafia," he audibly yawns. "This has always been the case." It had been the case, the hedge funder says, back in 2012, when he was raising money from a venture capitalist whose office was staffed with dozens of "attractive, strong young men," all of whom were "under 30" and looked as though they had freshly decamped from "the high school debate club." "They were all sleeping with each other and starting companies," he says. And it is absolutely the case now, he adds, when gay men are running influential companies in Silicon Valley and maintain entire social calendars with scarcely a straight man, much less a woman, in sight. "Of course the gay tech mafia exists," he continues. "This is not some Illuminati conspiracy theory. And you do not have to be gay to join. They like straight guys who sleep with them even more." Ever since I started covering Silicon Valley in 2017, I've heard variations of this rumor--that "gays," as an AI founder named Emmett Chen-Ran has quipped, "run this joint." On its face, a gay tech mafia seemed too dumb to warrant actual investigative inquiry.

High-dimensional decision-making tasks, such as business partner selection, involve evaluating large candidate pools with heterogeneous numerical, categorical, and textual features. MAS, a hierarchical multi-agent framework that decomposes evaluation into three layers: a Planner Agent that designs strategies, Specialized Agents that perform role-specific assessments, and a Supervisor Agent that integrates their outputs. To support systematic evaluation, we also introduce a curated benchmark dataset of venture capital co-investments, featuring diverse firm attributes and ground-truth syndicates. MAS consistently outperforms single-agent and debate-based multi-agent baselines, achieving up to 10-15% higher match rates. Analysis of agent reasoning shows that planners are most responsive to domain-informed prompts, specialists produce complementary feature coverage, and supervisors play an important role in aggregation. Our implementation is available at this anonymous link. In real-world decision-making, practitioners often navigate high-dimensional data including extensive option sets and numerous evaluative features (Sandanayake et al., 2018; Sigle et al., 2023). Business partner selection which includes partner shortlisting and strategic alliance formation exemplifies this challenge (Mindruta et al., 2016): firms often face a vast pool of potential candidates, each described by diverse attributes ranging from quantitative indicators (e.g., financial metrics, geographic presence) to text-rich information (e.g., strategic fit, investment preferences) (Shah & Swaminathan, 2008). The scale and complexity of such data can easily overwhelm human decision-makers, incurring significant costs (Li et al., 2008). This underscores the need for intelligent systems capable of analyzing large candidate sets and diverse features. Large language models (LLMs) have emerged as promising tools for addressing reasoning tasks in data-rich domains (Lee et al., 2025; Mischler et al., 2024). With appropriate prompting (e.g., few-shot learning) or information retrieval techniques (e.g., RAG), these models can identify salient features using only feature and task descriptions, achieving performance comparable to established methods (Li et al., 2025a; Jeong et al., 2024).

Predicting the success of start-up companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging this heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer rich reasoning abilities but struggle to adapt directly to domain-specific business data. We present \textbf{CrunchLLM}, a domain-adapted LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning strategies alongside prompt optimization to specialize foundation models for entrepreneurship data. Our approach achieves accuracy exceeding 80\% on Crunchbase startup success prediction, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM provides interpretable reasoning traces that justify its predictions, enhancing transparency and trustworthiness for financial and policy decision makers. This work demonstrates how adapting LLMs with domain-aware fine-tuning and structured--unstructured data fusion can advance predictive modeling of entrepreneurial outcomes. CrunchLLM contributes a methodological framework and a practical tool for data-driven decision making in venture capital and innovation policy.

Demand for AI chips is booming--and so is the need for software to run them. Chris Lattner's startup Modular just raised $250 million to build the best developer tools for AI hardware. At a certain point between building Apple's developer tools, leading a core part of Google's AI infrastructure team, and clashing with Elon Musk during a stint as Tesla's Autopilot chief, Chris Lattner's vision for his life's work started to come into focus. AI was taking over the world, and demand was growing for the chips that powered it. But the software stack for those chips was dominated by just a few big companies.

Venture capitalist Alexa von Tobel is ready to bet on quantum computing--starting with hardware. Alexa von Tobel is fully aware that her big bet on quantum computing may never pay off. "The risk of being too early is a real risk," she says. She's speaking to me via Zoom from the New York City office of Inspired Capital, the early-stage venture capital firm she runs with former US commerce secretary Penny Pritzker. In addition to personally investing in blue-chip brands like Uber and Airtable, von Tobel has backed a number of AI startups through Inspired Capital, including BrightAI (a platform that monitors critical infrastructure) and PreemptiveAI (a startup building a foundation model to map human physiology and predict health outcomes).

Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark for predicting founder success in venture capital (VC), a domain where signals are sparse, outcomes are uncertain, and even top investors perform modestly. At inception, the market index achieves a precision of 1.9%. Y Combinator outperforms the index by a factor of 1.7x, while tier-1 firms are 2.9x better. VCBench provides 9,000 anonymized founder profiles, standardized to preserve predictive features while resisting identity leakage, with adversarial tests showing more than 90% reduction in re-identification risk. We evaluate nine state-of-the-art large language models (LLMs). DeepSeek-V3 delivers over six times the baseline precision, GPT-4o achieves the highest F0.5, and most models surpass human benchmarks. Designed as a public and evolving resource available at vcbench.com, VCBench establishes a community-driven standard for reproducible and privacy-preserving evaluation of AGI in early-stage venture forecasting.

Predicting rare outcomes such as startup success is central to venture capital, demanding models that are both accurate and interpretable. We introduce Random Rule Forest (RRF), a lightweight ensemble method that uses a large language model (LLM) to generate simple YES/NO questions in natural language. Each question functions as a weak learner, and their responses are combined using a threshold-based voting rule to form a strong, interpretable predictor. Applied to a dataset of 9,892 founders, RRF achieves a 6.9x improvement over a random baseline on held-out data; adding expert-crafted questions lifts this to 8x and highlights the value of human-LLM collaboration. Compared with zero- and few-shot baselines across three LLM architectures, RRF attains an F0.5 of 0.121, versus 0.086 for the best baseline (+0.035 absolute, +41% relative). By combining the creativity of LLMs with the rigor of ensemble learning, RRF delivers interpretable, high-precision predictions suitable for decision-making in high-stakes domains.

This paper presents a framework for predicting rare, high-impact outcomes by integrating large language models (LLMs) with a multi-model machine learning (ML) architecture. The approach combines the predictive strength of black-box models with the interpretability required for reliable decision-making. We use LLM-powered feature engineering to extract and synthesize complex signals from unstructured data, which are then processed within a layered ensemble of models including XGBoost, Random Forest, and Linear Regression. The ensemble first produces a continuous estimate of success likelihood, which is then thresholded to produce a binary rare-event prediction. We apply this framework to the domain of Venture Capital (VC), where investors must evaluate startups with limited and noisy early-stage data. The empirical results show strong performance: the model achieves precision between 9.8X and 11.1X the random classifier baseline in three independent test subsets. Feature sensitivity analysis further reveals interpretable success drivers: the startup's category list accounts for 15.6% of predictive influence, followed by the number of founders, while education level and domain expertise contribute smaller yet consistent effects.

Media reputation plays an important role in attracting venture capital investment. However, prior research has focused too narrowly on general media exposure, limiting our understanding of how media truly influences funding decisions. As informed decision-makers, venture capitalists respond to more nuanced aspects of media content. We introduce the concept of media memorability - the media's ability to imprint a startup's name in the memory of relevant investors. Using data from 197 UK startups in the micro and nanotechnology sector (funded between 1995 and 2004), we show that media memorability significantly influences investment outcomes. Our findings suggest that venture capitalists rely on detailed cues such as a startup's distinctiveness and connectivity within news semantic networks. This contributes to research on entrepreneurial finance and media legitimation. In practice, startups should go beyond frequent media mentions to strengthen brand memorability through more targeted, meaningful coverage highlighting their uniqueness and relevance within the broader industry conversation.